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Credibility Assessment of Textual Claims on the Web

Authors:

Subhabrata Mukherjee,

Jannik Strötgen,

Gerhard WeikumAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2173 - 2178

https://doi.org/10.1145/2983323.2983661

Published: 24 October 2016 Publication History

Abstract

There is an increasing amount of false claims in news, social media, and other web sources. While prior work on truth discovery has focused on the case of checking factual statements, this paper addresses the novel task of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting without any assumptions about the structure of the claim, or the community where it is made. Our solution is based on automatically finding sources in news and social media, and feeding these into a distantly supervised classifier for assessing the credibility of a claim (i.e., true or fake). For inference, our method leverages the joint interaction between the language of articles about the claim and the reliability of the underlying web sources. Experiments with claims from the popular website snopes.com and from reported cases of Wikipedia hoaxes demonstrate the viability of our methods and their superior accuracy over various baselines.

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Cited By

Tran VSebastian YKarim AAzam S(2024)Distinguishing Human Journalists from Artificial Storytellers Through Stylistic FingerprintsComputers10.3390/computers1312032813:12(328)Online publication date: 5-Dec-2024
https://doi.org/10.3390/computers13120328
Zeng FGao W(2024)JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world ClaimsTransactions of the Association for Computational Linguistics10.1162/tacl_a_0064912(334-354)Online publication date: 5-Apr-2024
https://doi.org/10.1162/tacl_a_00649
Chrysidis ZPapadopoulos SPapadopoulos SPetrantonakis P(2024)Credible, Unreliable or Leaked?: Evidence verification for enhanced automated fact-checkingProceedings of the 3rd ACM International Workshop on Multimedia AI against Disinformation10.1145/3643491.3660278(73-81)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643491.3660278
Show More Cited By

Index Terms

Credibility Assessment of Textual Claims on the Web
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. World Wide Web

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Tran VSebastian YKarim AAzam S(2024)Distinguishing Human Journalists from Artificial Storytellers Through Stylistic FingerprintsComputers10.3390/computers1312032813:12(328)Online publication date: 5-Dec-2024
https://doi.org/10.3390/computers13120328
Zeng FGao W(2024)JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world ClaimsTransactions of the Association for Computational Linguistics10.1162/tacl_a_0064912(334-354)Online publication date: 5-Apr-2024
https://doi.org/10.1162/tacl_a_00649
Chrysidis ZPapadopoulos SPapadopoulos SPetrantonakis P(2024)Credible, Unreliable or Leaked?: Evidence verification for enhanced automated fact-checkingProceedings of the 3rd ACM International Workshop on Multimedia AI against Disinformation10.1145/3643491.3660278(73-81)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643491.3660278
Shahi GJaiswal ANandini DIbáñEz LMandl TLiu H(2024)Report on the 1st Workshop on Diffusion of Harmful Content on Online Web (DHOW) at WebSci 2024Companion Publication of the 16th ACM Web Science Conference10.1145/3630744.3665312(60-64)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3665312
Rashid JKim JMasood AChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Unraveling the Tangle of Disinformation: A Multimodal Approach for Fake News Identification on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651972(1849-1853)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651972
KRIVAA LCOSCIA M(2024)TRACES OF UNEQUAL ENTRY REQUIREMENT FOR ILLUSTRIOUS PEOPLE ON WIKIPEDIA BASED ON THEIR GENDERAdvances in Complex Systems10.1142/S021952592450003627:03Online publication date: 14-Jun-2024
https://doi.org/10.1142/S0219525924500036
Wu JXu WLiu QWu SWang L(2024)Adversarial Contrastive Learning for Evidence-Aware Fake News Detection With Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334164036:11(5591-5604)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341640
Liu QJin YCao XLiu XZhou XZhang YXu XQi L(2024)An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility AssessmentIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334287311:4(5308-5318)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3342873
El-Amrany SBrust MPecero JBouvry P(2024)Tri-FusionDet: Leveraging User Engagement, Textual, and Visual Features for Enhanced Fake News Detection2024 28th International Computer Science and Engineering Conference (ICSEC)10.1109/ICSEC62781.2024.10770746(1-6)Online publication date: 6-Nov-2024
https://doi.org/10.1109/ICSEC62781.2024.10770746
Peng LJian SKan ZQiao LLi D(2024)Not all fake news is semantically similar: Contextual semantic representation learning for multimodal fake news detectionInformation Processing & Management10.1016/j.ipm.2023.10356461:1(103564)Online publication date: Jan-2024
https://doi.org/10.1016/j.ipm.2023.103564
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